Trying to accurately predict results of fusion experiments by means of computer simulations has long been a formidable challenge in both inertial and magnetic confinement approaches. This thesis evaluates and builds on three different models suggested for approximating 'nonlocal' corrections to electron heat transport that arise due to presence of steep temperature gradients: Schurtz, Nicolaï and Busquet's multigroup diffusion model (SNB), Ji and Held's moment based approach (EIC), and the non-Fourier Landau-fluid model of Dimits, Joseph and Umansky (NFLF). It is found that, while the EIC and NFLF models are most successful in matching fully kinetic behaviour for small relative temperature perturbations to high degrees of nonlocality, they overestimate the peak heat flow by as much as 35% and fail to predict preheat in more realistic test problems where relative temperature differences are large. Instead, the popular SNB model proves to be more reliable in such situations with the caveat that its optimal implementation is found to differ significantly in its predictions from that typically used in rad-hydro codes. These conclusions are supported by a number of test problems benchmarked against Vlasov-Fokker-Planck simulations as well as a thorough mathematical analysis of the damping of low-amplitude temperature sinusoids. The majority of test problems presented will be more relevant to indirect drive inertial fusion, but consequences of modelling nonlocality in tokamak heat exhausts shall also be briefly considered. Furthermore, the consequence of incorporating the identified optimal implementation of the SNB model in the LLNL code HYDRA is considered. Finally, a simple method to incorporate nonlocal effects on the Nernst advection of magnetic fields down steep temperature gradients is presented, based on the assumption that the relationship between the Nernst velocity and the heat flow velocity is unaffected by nonlocality. The effectiveness of this method is demonstrated in a number of inertial fusion scenarios.